DocumentCode :
79940
Title :
A Unifying Framework of Mining Trajectory Patterns of Various Temporal Tightness
Author :
Jae-Gil Lee ; Jiawei Han ; Xiaolei Li
Author_Institution :
Dept. of Knowledge Service Eng., KAIST, Daejeon, South Korea
Volume :
27
Issue :
6
fYear :
2015
fDate :
June 1 2015
Firstpage :
1478
Lastpage :
1490
Abstract :
Discovering trajectory patterns is shown to be very useful in learning interactions between moving objects. Many types of trajectory patterns have been proposed in the literature, but previous methods were developed for only a specific type of trajectory patterns. This limitation could make pattern discovery tedious and inefficient since users typically do not know which types of trajectory patterns are hidden in their data sets. Our main observation is that many trajectory patterns can be arranged according to the strength of temporal constraints. In this paper, we propose a unifying framework of mining trajectory patterns of various temporal tightness, which we call unifying trajectory patterns (UT-patterns). This framework consists of two phases: initial pattern discovery and granularity adjustment. A set of initial patterns are discovered in the first phase, and their granularities (i.e., levels of detail) are adjusted by split and merge to detect other types in the second phase. As a result, the structure called a pattern forest is constructed to show various patterns. Both phases are guided by an information-theoretic formula without user intervention. Experimental results demonstrate that our framework facilitates easy discovery of various patterns from real-world trajectory data.
Keywords :
data mining; learning (artificial intelligence); pattern clustering; granularity adjustment; information-theoretic formula; initial pattern discovery; learning interactions; moving objects; pattern forest; temporal tightness; trajectory pattern discovery; trajectory pattern mining; Animals; Clustering algorithms; Data mining; Electronic mail; Partitioning algorithms; Trajectory; Vectors; Trajectory pattern mining; moving object trajectories; synchronous movement patterns; trajectory clustering;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2014.2377742
Filename :
6977963
Link To Document :
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